A Logistic Regression Model for Biomechanical Risk Classification in Lifting Tasks

Donisi, Leandro; Cesarelli, Giuseppe; Capodaglio, E; Panigazzi, Monica; D’Addio, Giovanni; Cesarelli, Mario; Amato, Francesco

doi:10.3390/diagnostics12112624

Cited by 13 publications

(3 citation statements)

References 51 publications

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“…Donisi et al [ 31 ] proposed a methodology to discriminate biomechanical risk classes according to the RNLE using time-domain features extracted from inertial signals (acceleration and angular velocity) acquired by means of an IMU placed on the lumbar zone, reaching accuracy and AUCROC values greater than 90% and 94%, respectively. In another study, Donisi et al [ 65 ] explored the feasibility of a logistic regression model fed with time- and frequency- domain features extracted from signals acquired using one IMU placed on the sternum to classify risk classes associated with lifting activities according to the RNLE, reaching an accuracy equal to 82.8%. Differently from the present study, the authors used inertial signals.…”

Section: Discussionmentioning

confidence: 99%

sEMG Spectral Analysis and Machine Learning Algorithms Are Able to Discriminate Biomechanical Risk Classes Associated with Manual Material Liftings

Donisi,

Jacob,

Guerrini

et al. 2023

Bioengineering

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Manual material handling and load lifting are activities that can cause work-related musculoskeletal disorders. For this reason, the National Institute for Occupational Safety and Health proposed an equation depending on the following parameters: intensity, duration, frequency, and geometric characteristics associated with the load lifting. In this paper, we explore the feasibility of several Machine Learning (ML) algorithms, fed with frequency-domain features extracted from electromyographic (EMG) signals of back muscles, to discriminate biomechanical risk classes defined by the Revised NIOSH Lifting Equation. The EMG signals of the multifidus and erector spinae muscles were acquired by means of a wearable device for surface EMG and then segmented to extract several frequency-domain features relating to the Total Power Spectrum of the EMG signal. These features were fed to several ML algorithms to assess their prediction power. The ML algorithms produced interesting results in the classification task, with the Support Vector Machine algorithm outperforming the others with accuracy and Area under the Receiver Operating Characteristic Curve values of up to 0.985. Moreover, a correlation between muscular fatigue and risky lifting activities was found. These results showed the feasibility of the proposed methodology—based on wearable sensors and artificial intelligence—to predict the biomechanical risk associated with load lifting. A future investigation on an enriched study population and additional lifting scenarios could confirm the potential of the proposed methodology and its applicability in the field of occupational ergonomics.

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Section: Discussionmentioning

confidence: 99%

sEMG Spectral Analysis and Machine Learning Algorithms Are Able to Discriminate Biomechanical Risk Classes Associated with Manual Material Liftings

Donisi,

Jacob,

Guerrini

et al. 2023

Bioengineering

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“…"Manual material handling" poses various risks, and it is vital to include it in a hoisting risk assessment. "Musculoskeletal disorder" is an injury or illness that affects the musculoskeletal system, and occurs due to repetitive or awkward movements, constant poor posture, or physical exertion, leading to pain, discomfort, and functional limitations [65,66]. "Backache" is one of the most prevalent types of work-related musculoskeletal disorders (WMSDs) and a risk factor [67,68].…”

Section: Topic Trendsmentioning

confidence: 99%

A Bibliometrics-Based Systematic Review of Safety Risk Assessment for IBS Hoisting Construction

Junjia,

Alias,

Haron

et al. 2023

Buildings

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Construction faces many safety accidents with urbanization, particularly in hoisting. However, there is a lack of systematic review studies in this area. This paper explored the factors and methods of risk assessment in hoisting for industrial building system (IBS) construction. Firstly, bibliometric analysis revealed that future research will focus on “ergonomics”, “machine learning”, “computer simulation”, and “wearable sensors”. Secondly, the previous 80 factors contributing to hoisting risks were summarized from a “human–equipment–management–material–environment” perspective, which can serve as a reference point for managers. Finally, we discussed, in-depth, the application of artificial neural networks (ANNs) and digital twins (DT). ANNs have improved the efficiency and accuracy of risk assessment. Still, they require high-quality and significant data, which traditional methods do not provide, resulting in the low accuracy of risk simulation results. DT data are emerging as an alternative, enabling stakeholders to visualize and analyze the construction process. However, DT’s interactivity, high cost, and information security need further improvement. Based on the discussion and analysis, the risk control model created in this paper guides the direction for future research.

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“…Additionally, the integration of wearable sensors and artificial intelligence (AI) algorithms is increasingly strengthening in the field of occupational ergonomics, as has been reported in several scientific works. For instance, Donisi et al [ 22 ] proposed a methodology, based on machine learning (ML) models fed with time- and frequency- domain features extracted from inertial signals acquired from the sternum, to classify biomechanical risk during lifting tasks according to the Revised NIOSH (National Institute for Occupational Safety and Health) Lifting Equation. They employed a logistic regression (LR) model, reaching an accuracy classification equal to 82.8%.…”

Section: Introductionmentioning

confidence: 99%

Capability of Machine Learning Algorithms to Classify Safe and Unsafe Postures during Weight Lifting Tasks Using Inertial Sensors

Prisco,

Romano,

Esposito

et al. 2024

Diagnostics

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Occupational ergonomics aims to optimize the work environment and to enhance both productivity and worker well-being. Work-related exposure assessment, such as lifting loads, is a crucial aspect of this discipline, as it involves the evaluation of physical stressors and their impact on workers’ health and safety, in order to prevent the development of musculoskeletal pathologies. In this study, we explore the feasibility of machine learning (ML) algorithms, fed with time- and frequency-domain features extracted from inertial signals (linear acceleration and angular velocity), to automatically and accurately discriminate safe and unsafe postures during weight lifting tasks. The signals were acquired by means of one inertial measurement unit (IMU) placed on the sternums of 15 subjects, and subsequently segmented to extract several time- and frequency-domain features. A supervised dataset, including the extracted features, was used to feed several ML models and to assess their prediction power. Interesting results in terms of evaluation metrics for a binary safe/unsafe posture classification were obtained with the logistic regression algorithm, which outperformed the others, with accuracy and area under the receiver operating characteristic curve values of up to 96% and 99%, respectively. This result indicates the feasibility of the proposed methodology—based on a single inertial sensor and artificial intelligence—to discriminate safe/unsafe postures associated with load lifting activities. Future investigation in a wider study population and using additional lifting scenarios could confirm the potentiality of the proposed methodology, supporting its applicability in the occupational ergonomics field.

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A Logistic Regression Model for Biomechanical Risk Classification in Lifting Tasks

Cited by 13 publications

References 51 publications

sEMG Spectral Analysis and Machine Learning Algorithms Are Able to Discriminate Biomechanical Risk Classes Associated with Manual Material Liftings

sEMG Spectral Analysis and Machine Learning Algorithms Are Able to Discriminate Biomechanical Risk Classes Associated with Manual Material Liftings

A Bibliometrics-Based Systematic Review of Safety Risk Assessment for IBS Hoisting Construction

Capability of Machine Learning Algorithms to Classify Safe and Unsafe Postures during Weight Lifting Tasks Using Inertial Sensors

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